Brief Description:
This seminar is intended as a follow-up to
the version of Q550 Prof. Kruschke taught in Spring 2003. Other
students with previous experience modeling data are also
welcome. ¶ In this course we will explore the mechanics of
fitting models to data, and we will delve into theoretical issues in
deciding which models fit best. We will consider topics such as
different measures of discrepancy between data and model predictions,
different algorithms for finding best-fitting parameter values,
estimating confidence intervals for parameter values, detecting and
dealing with parameter redundancy, measures of model complexity, and
criteria for model selection. ¶ The material will be discussed
mostly at a general, methodological level, with only occasional
application to specific models in cognitive science. Lots of "hands
on" experience will be gained via work with MATLAB. See the schedule page for more details about topics
and lots of cool pictures.

Other Readings: Recent articles available online
to IU students. Among these will be articles by In-Jae Myung regarding
model complexity. Details TBA.

Software: We will make extensive use of MATLAB.
MATLAB is available on all IU public cluster computers. Details of
campus availability can be found from the
Stat-Math Center. MATLAB is available for purchase in student
versions, see its
manufacturer, MathWorks.

A free Matlab-alike, called Octave, is also available: http://www.octave.org/. (Thanks to
Christoph Weidemann for informing me of Octave.)

Registration Info: Course number P747, section
3885. Requires graduate standing in a field related to cognitive
science or permission of the instructor.